Overview

Dataset statistics

Number of variables15
Number of observations112650
Missing cells6484
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 MiB
Average record size in memory128.0 B

Variable types

Categorical5
Numeric10

Alerts

product_id has a high cardinality: 32951 distinct valuesHigh cardinality
product_category_name has a high cardinality: 73 distinct valuesHigh cardinality
order_id has a high cardinality: 98666 distinct valuesHigh cardinality
seller_id has a high cardinality: 3095 distinct valuesHigh cardinality
shipping_limit_date has a high cardinality: 93318 distinct valuesHigh cardinality
product_weight_g is highly overall correlated with product_length_cm and 3 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
price is highly overall correlated with product_weight_gHigh correlation
product_category_name has 1603 (1.4%) missing valuesMissing
product_name_lenght has 1603 (1.4%) missing valuesMissing
product_description_lenght has 1603 (1.4%) missing valuesMissing
product_photos_qty has 1603 (1.4%) missing valuesMissing
order_id is uniformly distributedUniform
shipping_limit_date is uniformly distributedUniform

Reproduction

Analysis started2023-06-23 13:25:24.786977
Analysis finished2023-06-23 13:25:48.503480
Duration23.72 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

product_id
Categorical

Distinct32951
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
527
99a4788cb24856965c36a24e339b6058
 
488
422879e10f46682990de24d770e7f83d
 
484
389d119b48cf3043d311335e499d9c6b
 
392
368c6c730842d78016ad823897a372db
 
388
Other values (32946)
110371 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3604800
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18117 ?
Unique (%)16.1%

Sample

1st row1e9e8ef04dbcff4541ed26657ea517e5
2nd row3aa071139cb16b67ca9e5dea641aaa2f
3rd row96bd76ec8810374ed1b65e291975717f
4th rowcef67bcfe19066a932b7673e239eb23d
5th row9dc1a7de274444849c219cff195d0b71

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 527
 
0.5%
99a4788cb24856965c36a24e339b6058 488
 
0.4%
422879e10f46682990de24d770e7f83d 484
 
0.4%
389d119b48cf3043d311335e499d9c6b 392
 
0.3%
368c6c730842d78016ad823897a372db 388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 343
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 323
 
0.3%
154e7e31ebfa092203795c972e5804a6 281
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 274
 
0.2%
Other values (32941) 108777
96.6%

Length

2023-06-23T10:25:48.582989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 527
 
0.5%
99a4788cb24856965c36a24e339b6058 488
 
0.4%
422879e10f46682990de24d770e7f83d 484
 
0.4%
389d119b48cf3043d311335e499d9c6b 392
 
0.3%
368c6c730842d78016ad823897a372db 388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 343
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 323
 
0.3%
154e7e31ebfa092203795c972e5804a6 281
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 274
 
0.2%
Other values (32941) 108777
96.6%

Most occurring characters

ValueCountFrequency (%)
3 231812
 
6.4%
9 229514
 
6.4%
e 227504
 
6.3%
7 227012
 
6.3%
8 226818
 
6.3%
4 226338
 
6.3%
a 225916
 
6.3%
c 225040
 
6.2%
0 224967
 
6.2%
2 224937
 
6.2%
Other values (6) 1334942
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2262781
62.8%
Lowercase Letter 1342019
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 231812
10.2%
9 229514
10.1%
7 227012
10.0%
8 226818
10.0%
4 226338
10.0%
0 224967
9.9%
2 224937
9.9%
5 224337
9.9%
6 224102
9.9%
1 222944
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 227504
17.0%
a 225916
16.8%
c 225040
16.8%
b 223674
16.7%
d 221547
16.5%
f 218338
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2262781
62.8%
Latin 1342019
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 231812
10.2%
9 229514
10.1%
7 227012
10.0%
8 226818
10.0%
4 226338
10.0%
0 224967
9.9%
2 224937
9.9%
5 224337
9.9%
6 224102
9.9%
1 222944
9.9%
Latin
ValueCountFrequency (%)
e 227504
17.0%
a 225916
16.8%
c 225040
16.8%
b 223674
16.7%
d 221547
16.5%
f 218338
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3604800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 231812
 
6.4%
9 229514
 
6.4%
e 227504
 
6.3%
7 227012
 
6.3%
8 226818
 
6.3%
4 226338
 
6.3%
a 225916
 
6.3%
c 225040
 
6.2%
0 224967
 
6.2%
2 224937
 
6.2%
Other values (6) 1334942
37.0%

product_category_name
Categorical

HIGH CARDINALITY  MISSING 

Distinct73
Distinct (%)0.1%
Missing1603
Missing (%)1.4%
Memory size1.7 MiB
cama_mesa_banho
11115 
beleza_saude
9670 
esporte_lazer
8641 
moveis_decoracao
8334 
informatica_acessorios
7827 
Other values (68)
65460 

Length

Max length46
Median length32
Mean length14.861113
Min length3

Characters and Unicode

Total characters1650282
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowperfumaria
2nd rowartes
3rd rowesporte_lazer
4th rowbebes
5th rowutilidades_domesticas

Common Values

ValueCountFrequency (%)
cama_mesa_banho 11115
 
9.9%
beleza_saude 9670
 
8.6%
esporte_lazer 8641
 
7.7%
moveis_decoracao 8334
 
7.4%
informatica_acessorios 7827
 
6.9%
utilidades_domesticas 6964
 
6.2%
relogios_presentes 5991
 
5.3%
telefonia 4545
 
4.0%
ferramentas_jardim 4347
 
3.9%
automotivo 4235
 
3.8%
Other values (63) 39378
35.0%

Length

2023-06-23T10:25:48.694690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cama_mesa_banho 11115
 
10.0%
beleza_saude 9670
 
8.7%
esporte_lazer 8641
 
7.8%
moveis_decoracao 8334
 
7.5%
informatica_acessorios 7827
 
7.0%
utilidades_domesticas 6964
 
6.3%
relogios_presentes 5991
 
5.4%
telefonia 4545
 
4.1%
ferramentas_jardim 4347
 
3.9%
automotivo 4235
 
3.8%
Other values (63) 39378
35.5%

Most occurring characters

ValueCountFrequency (%)
e 201012
12.2%
a 197895
12.0%
s 164475
10.0%
o 163286
9.9%
i 109732
 
6.6%
r 106492
 
6.5%
_ 104608
 
6.3%
t 79595
 
4.8%
c 78128
 
4.7%
m 74189
 
4.5%
Other values (18) 370870
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1545406
93.6%
Connector Punctuation 104608
 
6.3%
Decimal Number 268
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 201012
13.0%
a 197895
12.8%
s 164475
10.6%
o 163286
10.6%
i 109732
 
7.1%
r 106492
 
6.9%
t 79595
 
5.2%
c 78128
 
5.1%
m 74189
 
4.8%
n 56353
 
3.6%
Other values (16) 314249
20.3%
Connector Punctuation
ValueCountFrequency (%)
_ 104608
100.0%
Decimal Number
ValueCountFrequency (%)
2 268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1545406
93.6%
Common 104876
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 201012
13.0%
a 197895
12.8%
s 164475
10.6%
o 163286
10.6%
i 109732
 
7.1%
r 106492
 
6.9%
t 79595
 
5.2%
c 78128
 
5.1%
m 74189
 
4.8%
n 56353
 
3.6%
Other values (16) 314249
20.3%
Common
ValueCountFrequency (%)
_ 104608
99.7%
2 268
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1650282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 201012
12.2%
a 197895
12.0%
s 164475
10.0%
o 163286
9.9%
i 109732
 
6.6%
r 106492
 
6.5%
_ 104608
 
6.3%
t 79595
 
4.8%
c 78128
 
4.7%
m 74189
 
4.5%
Other values (18) 370870
22.5%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing1603
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean48.775978
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:48.843245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.025581
Coefficient of variation (CV)0.20554341
Kurtosis0.15660995
Mean48.775978
Median Absolute Deviation (MAD)6
Skewness-0.90714361
Sum5416426
Variance100.51227
MonotonicityNot monotonic
2023-06-23T10:25:49.003195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8322
 
7.4%
60 7713
 
6.8%
56 6504
 
5.8%
58 6435
 
5.7%
57 5993
 
5.3%
55 5541
 
4.9%
54 5265
 
4.7%
53 4179
 
3.7%
52 4158
 
3.7%
49 3556
 
3.2%
Other values (56) 53381
47.4%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 14
 
< 0.1%
10 5
 
< 0.1%
11 10
 
< 0.1%
12 38
< 0.1%
13 26
< 0.1%
14 45
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 2
 
< 0.1%
66 1
 
< 0.1%
64 164
 
0.1%
63 1261
1.1%
62 156
 
0.1%
61 235
 
0.2%
Distinct2960
Distinct (%)2.7%
Missing1603
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean787.86703
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:49.202996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile161
Q1348
median603
Q3987
95-th percentile2124
Maximum3992
Range3988
Interquartile range (IQR)639

Descriptive statistics

Standard deviation652.13561
Coefficient of variation (CV)0.82772293
Kurtosis4.9012551
Mean787.86703
Median Absolute Deviation (MAD)294
Skewness2.0055505
Sum87490270
Variance425280.85
MonotonicityNot monotonic
2023-06-23T10:25:49.404707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 690
 
0.6%
1893 636
 
0.6%
348 620
 
0.6%
903 583
 
0.5%
492 564
 
0.5%
245 547
 
0.5%
366 523
 
0.5%
236 489
 
0.4%
340 465
 
0.4%
919 426
 
0.4%
Other values (2950) 105504
93.7%
(Missing) 1603
 
1.4%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 6
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 5
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing1603
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.209713
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:49.575031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7214385
Coefficient of variation (CV)0.77903259
Kurtosis4.8347748
Mean2.209713
Median Absolute Deviation (MAD)0
Skewness1.9079075
Sum245382
Variance2.9633504
MonotonicityNot monotonic
2023-06-23T10:25:49.665052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 56028
49.7%
2 21963
 
19.5%
3 12392
 
11.0%
4 8437
 
7.5%
5 5368
 
4.8%
6 3786
 
3.4%
7 1501
 
1.3%
8 727
 
0.6%
10 342
 
0.3%
9 313
 
0.3%
Other values (9) 190
 
0.2%
(Missing) 1603
 
1.4%
ValueCountFrequency (%)
1 56028
49.7%
2 21963
 
19.5%
3 12392
 
11.0%
4 8437
 
7.5%
5 5368
 
4.8%
6 3786
 
3.4%
7 1501
 
1.3%
8 727
 
0.6%
9 313
 
0.3%
10 342
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 53
 
< 0.1%
11 71
 
0.1%
10 342
0.3%

product_weight_g
Real number (ℝ)

Distinct2204
Distinct (%)2.0%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2093.672
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:49.791642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9750
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3751.5969
Coefficient of variation (CV)1.7918742
Kurtosis16.264762
Mean2093.672
Median Absolute Deviation (MAD)500
Skewness3.598715
Sum2.3581447 Ă— 108
Variance14074479
MonotonicityNot monotonic
2023-06-23T10:25:49.997052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6778
 
6.0%
150 5276
 
4.7%
250 4520
 
4.0%
300 4258
 
3.8%
400 3613
 
3.2%
100 3490
 
3.1%
350 3161
 
2.8%
500 2696
 
2.4%
600 2695
 
2.4%
700 2050
 
1.8%
Other values (2194) 74095
65.8%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 949
0.8%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 282
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.153669
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:50.441929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.153449
Coefficient of variation (CV)0.53570427
Kurtosis3.7454877
Mean30.153669
Median Absolute Deviation (MAD)8
Skewness1.761463
Sum3396268
Variance260.93392
MonotonicityNot monotonic
2023-06-23T10:25:50.563041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 17603
 
15.6%
20 10529
 
9.3%
30 7558
 
6.7%
17 5963
 
5.3%
18 5727
 
5.1%
25 4676
 
4.2%
19 4661
 
4.1%
40 4090
 
3.6%
22 3827
 
3.4%
50 2967
 
2.6%
Other values (89) 45031
40.0%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 95
 
0.1%
12 40
 
< 0.1%
13 60
 
0.1%
14 135
 
0.1%
15 202
 
0.2%
16 17603
15.6%
ValueCountFrequency (%)
105 328
0.3%
104 33
 
< 0.1%
103 46
 
< 0.1%
102 58
 
0.1%
101 107
 
0.1%
100 381
0.3%
99 35
 
< 0.1%
98 47
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.593766
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:50.760360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.443483
Coefficient of variation (CV)0.81015265
Kurtosis7.3846878
Mean16.593766
Median Absolute Deviation (MAD)6
Skewness2.2538099
Sum1868989
Variance180.72724
MonotonicityNot monotonic
2023-06-23T10:25:50.911765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9826
 
8.7%
20 6566
 
5.8%
15 6558
 
5.8%
12 6263
 
5.6%
11 6147
 
5.5%
2 4997
 
4.4%
4 4668
 
4.1%
8 4660
 
4.1%
16 4577
 
4.1%
5 4560
 
4.0%
Other values (92) 53810
47.8%
ValueCountFrequency (%)
2 4997
4.4%
3 2710
 
2.4%
4 4668
4.1%
5 4560
4.0%
6 3396
 
3.0%
7 4202
3.7%
8 4660
4.1%
9 3201
 
2.8%
10 9826
8.7%
11 6147
5.5%
ValueCountFrequency (%)
105 134
0.1%
104 12
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 42
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.996546
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:51.042919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.707268
Coefficient of variation (CV)0.50908811
Kurtosis4.6869659
Mean22.996546
Median Absolute Deviation (MAD)6
Skewness1.7266011
Sum2590147
Variance137.06013
MonotonicityNot monotonic
2023-06-23T10:25:51.155617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12095
 
10.7%
11 10667
 
9.5%
15 8951
 
7.9%
16 8461
 
7.5%
30 7626
 
6.8%
12 5463
 
4.8%
13 5264
 
4.7%
14 4607
 
4.1%
18 4064
 
3.6%
40 3895
 
3.5%
Other values (85) 41539
36.9%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 28
 
< 0.1%
9 50
 
< 0.1%
10 82
 
0.1%
11 10667
9.5%
12 5463
4.8%
13 5264
4.7%
14 4607
4.1%
15 8951
7.9%
ValueCountFrequency (%)
118 8
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 42
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%

order_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct98666
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
8272b63d03f5f79c56e9e4120aec44ef
 
21
ab14fdcfbe524636d65ee38360e22ce8
 
20
1b15974a0141d54e36626dca3fdc731a
 
20
428a2f660dc84138d969ccd69a0ab6d5
 
15
9ef13efd6949e4573a18964dd1bbe7f5
 
15
Other values (98661)
112559 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3604800
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88863 ?
Unique (%)78.9%

Sample

1st rowe17e4f88e31525f7deef66779844ddce
2nd row5236307716393b7114b53ee991f36956
3rd row01f66e58769f84129811d43eefd187fb
4th row143d00a4f2dde4e0364ee1821577adb3
5th row86cafb8794cb99a9b1b77fc8e48fbbbb

Common Values

ValueCountFrequency (%)
8272b63d03f5f79c56e9e4120aec44ef 21
 
< 0.1%
ab14fdcfbe524636d65ee38360e22ce8 20
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a 20
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d5 15
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f5 15
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c 14
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a 14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b 13
 
< 0.1%
af822dacd6f5cff7376413c03a388bb7 12
 
< 0.1%
c05d6a79e55da72ca780ce90364abed9 12
 
< 0.1%
Other values (98656) 112494
99.9%

Length

2023-06-23T10:25:51.270859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8272b63d03f5f79c56e9e4120aec44ef 21
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a 20
 
< 0.1%
ab14fdcfbe524636d65ee38360e22ce8 20
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d5 15
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f5 15
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c 14
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a 14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b 13
 
< 0.1%
2c2a19b5703863c908512d135aa6accc 12
 
< 0.1%
637617b3ffe9e2f7a2411243829226d0 12
 
< 0.1%
Other values (98656) 112494
99.9%

Most occurring characters

ValueCountFrequency (%)
4 226438
 
6.3%
6 225940
 
6.3%
b 225897
 
6.3%
e 225872
 
6.3%
7 225739
 
6.3%
3 225711
 
6.3%
a 225498
 
6.3%
2 225476
 
6.3%
8 225358
 
6.3%
1 225287
 
6.2%
Other values (6) 1347584
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2252967
62.5%
Lowercase Letter 1351833
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 226438
10.1%
6 225940
10.0%
7 225739
10.0%
3 225711
10.0%
2 225476
10.0%
8 225358
10.0%
1 225287
10.0%
9 224719
10.0%
0 224470
10.0%
5 223829
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 225897
16.7%
e 225872
16.7%
a 225498
16.7%
c 225204
16.7%
f 225069
16.6%
d 224293
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2252967
62.5%
Latin 1351833
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 226438
10.1%
6 225940
10.0%
7 225739
10.0%
3 225711
10.0%
2 225476
10.0%
8 225358
10.0%
1 225287
10.0%
9 224719
10.0%
0 224470
10.0%
5 223829
9.9%
Latin
ValueCountFrequency (%)
b 225897
16.7%
e 225872
16.7%
a 225498
16.7%
c 225204
16.7%
f 225069
16.6%
d 224293
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3604800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 226438
 
6.3%
6 225940
 
6.3%
b 225897
 
6.3%
e 225872
 
6.3%
7 225739
 
6.3%
3 225711
 
6.3%
a 225498
 
6.3%
2 225476
 
6.3%
8 225358
 
6.3%
1 225287
 
6.2%
Other values (6) 1347584
37.4%

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.197834
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:51.362906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70512403
Coefficient of variation (CV)0.5886659
Kurtosis103.85736
Mean1.197834
Median Absolute Deviation (MAD)0
Skewness7.5803557
Sum134936
Variance0.4971999
MonotonicityNot monotonic
2023-06-23T10:25:51.457157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 98666
87.6%
2 9803
 
8.7%
3 2287
 
2.0%
4 965
 
0.9%
5 460
 
0.4%
6 256
 
0.2%
7 58
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 66
 
0.1%
ValueCountFrequency (%)
1 98666
87.6%
2 9803
 
8.7%
3 2287
 
2.0%
4 965
 
0.9%
5 460
 
0.4%
6 256
 
0.2%
7 58
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 5
 
< 0.1%
14 7
< 0.1%
13 8
< 0.1%
12 13
< 0.1%

seller_id
Categorical

Distinct3095
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
6560211a19b47992c3666cc44a7e94c0
 
2033
4a3ca9315b744ce9f8e9374361493884
 
1987
1f50f920176fa81dab994f9023523100
 
1931
cc419e0650a3c5ba77189a1882b7556a
 
1775
da8622b14eb17ae2831f4ac5b9dab84a
 
1551
Other values (3090)
103373 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3604800
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)0.5%

Sample

1st row5670f4db5b62c43d542e1b2d56b0cf7c
2nd rowb561927807645834b59ef0d16ba55a24
3rd row7b07b3c7487f0ea825fc6df75abd658b
4th rowc510bc1718f0f2961eaa42a23330681a
5th row0be8ff43f22e456b4e0371b2245e4d01

Common Values

ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c0 2033
 
1.8%
4a3ca9315b744ce9f8e9374361493884 1987
 
1.8%
1f50f920176fa81dab994f9023523100 1931
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1775
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1551
 
1.4%
955fee9216a65b617aa5c0531780ce60 1499
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1428
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1364
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc 1203
 
1.1%
7a67c85e85bb2ce8582c35f2203ad736 1171
 
1.0%
Other values (3085) 96708
85.8%

Length

2023-06-23T10:25:51.565955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c0 2033
 
1.8%
4a3ca9315b744ce9f8e9374361493884 1987
 
1.8%
1f50f920176fa81dab994f9023523100 1931
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1775
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1551
 
1.4%
955fee9216a65b617aa5c0531780ce60 1499
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1428
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1364
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc 1203
 
1.1%
7a67c85e85bb2ce8582c35f2203ad736 1171
 
1.0%
Other values (3085) 96708
85.8%

Most occurring characters

ValueCountFrequency (%)
1 244290
 
6.8%
c 237797
 
6.6%
4 236139
 
6.6%
6 232243
 
6.4%
0 231143
 
6.4%
a 229817
 
6.4%
b 229114
 
6.4%
3 228981
 
6.4%
9 223671
 
6.2%
2 222808
 
6.2%
Other values (6) 1288797
35.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2279606
63.2%
Lowercase Letter 1325194
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 244290
10.7%
4 236139
10.4%
6 232243
10.2%
0 231143
10.1%
3 228981
10.0%
9 223671
9.8%
2 222808
9.8%
5 220264
9.7%
8 220219
9.7%
7 219848
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 237797
17.9%
a 229817
17.3%
b 229114
17.3%
e 212401
16.0%
f 209103
15.8%
d 206962
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2279606
63.2%
Latin 1325194
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 244290
10.7%
4 236139
10.4%
6 232243
10.2%
0 231143
10.1%
3 228981
10.0%
9 223671
9.8%
2 222808
9.8%
5 220264
9.7%
8 220219
9.7%
7 219848
9.6%
Latin
ValueCountFrequency (%)
c 237797
17.9%
a 229817
17.3%
b 229114
17.3%
e 212401
16.0%
f 209103
15.8%
d 206962
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3604800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 244290
 
6.8%
c 237797
 
6.6%
4 236139
 
6.6%
6 232243
 
6.4%
0 231143
 
6.4%
a 229817
 
6.4%
b 229114
 
6.4%
3 228981
 
6.4%
9 223671
 
6.2%
2 222808
 
6.2%
Other values (6) 1288797
35.8%

shipping_limit_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct93318
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2018-03-01 02:50:48
 
21
2017-07-21 18:25:23
 
21
2017-08-30 14:30:23
 
20
2017-12-21 02:30:41
 
15
2017-11-30 10:30:51
 
15
Other values (93313)
112558 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2140350
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79836 ?
Unique (%)70.9%

Sample

1st row2018-04-30 17:33:54
2nd row2018-02-06 19:11:15
3rd row2018-07-11 21:30:20
4th row2018-08-07 09:10:13
5th row2018-04-17 01:30:23

Common Values

ValueCountFrequency (%)
2018-03-01 02:50:48 21
 
< 0.1%
2017-07-21 18:25:23 21
 
< 0.1%
2017-08-30 14:30:23 20
 
< 0.1%
2017-12-21 02:30:41 15
 
< 0.1%
2017-11-30 10:30:51 15
 
< 0.1%
2017-02-03 21:44:49 15
 
< 0.1%
2018-02-28 11:48:12 14
 
< 0.1%
2018-06-13 17:30:35 13
 
< 0.1%
2018-04-25 22:11:43 13
 
< 0.1%
2018-04-19 02:30:52 13
 
< 0.1%
Other values (93308) 112490
99.9%

Length

2023-06-23T10:25:51.656252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-30 1647
 
0.7%
2017-12-07 752
 
0.3%
2018-04-19 709
 
0.3%
2018-05-10 673
 
0.3%
2018-01-18 661
 
0.3%
2018-03-08 653
 
0.3%
2018-08-07 653
 
0.3%
2018-02-22 651
 
0.3%
2018-03-01 644
 
0.3%
2018-03-22 632
 
0.3%
Other values (40675) 217625
96.6%

Most occurring characters

ValueCountFrequency (%)
0 361652
16.9%
1 346105
16.2%
2 275987
12.9%
- 225300
10.5%
: 225300
10.5%
8 113614
 
5.3%
112650
 
5.3%
3 108862
 
5.1%
5 106657
 
5.0%
7 96681
 
4.5%
Other values (3) 167542
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1577100
73.7%
Dash Punctuation 225300
 
10.5%
Other Punctuation 225300
 
10.5%
Space Separator 112650
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 361652
22.9%
1 346105
21.9%
2 275987
17.5%
8 113614
 
7.2%
3 108862
 
6.9%
5 106657
 
6.8%
7 96681
 
6.1%
4 75623
 
4.8%
6 47204
 
3.0%
9 44715
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 225300
100.0%
Other Punctuation
ValueCountFrequency (%)
: 225300
100.0%
Space Separator
ValueCountFrequency (%)
112650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2140350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 361652
16.9%
1 346105
16.2%
2 275987
12.9%
- 225300
10.5%
: 225300
10.5%
8 113614
 
5.3%
112650
 
5.3%
3 108862
 
5.1%
5 106657
 
5.0%
7 96681
 
4.5%
Other values (3) 167542
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2140350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 361652
16.9%
1 346105
16.2%
2 275987
12.9%
- 225300
10.5%
: 225300
10.5%
8 113614
 
5.3%
112650
 
5.3%
3 108862
 
5.1%
5 106657
 
5.0%
7 96681
 
4.5%
Other values (3) 167542
7.8%

price
Real number (ℝ)

Distinct5968
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.65374
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:51.757926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.99
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation183.63393
Coefficient of variation (CV)1.5219912
Kurtosis120.8283
Mean120.65374
Median Absolute Deviation (MAD)42.09
Skewness7.9232083
Sum13591644
Variance33721.42
MonotonicityNot monotonic
2023-06-23T10:25:51.879329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2481
 
2.2%
69.9 1987
 
1.8%
49.9 1953
 
1.7%
89.9 1548
 
1.4%
99.9 1432
 
1.3%
39.9 1339
 
1.2%
29.9 1318
 
1.2%
79.9 1214
 
1.1%
19.9 1201
 
1.1%
29.99 1176
 
1.0%
Other values (5958) 97001
86.1%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 1
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6999
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.99032
Minimum0
Maximum409.68
Zeros383
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-06-23T10:25:52.006098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.26
Q321.15
95-th percentile45.12
Maximum409.68
Range409.68
Interquartile range (IQR)8.07

Descriptive statistics

Standard deviation15.806405
Coefficient of variation (CV)0.79070297
Kurtosis59.788253
Mean19.99032
Median Absolute Deviation (MAD)3.61
Skewness5.6398696
Sum2251909.5
Variance249.84245
MonotonicityNot monotonic
2023-06-23T10:25:52.120637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3707
 
3.3%
7.78 2262
 
2.0%
14.1 1875
 
1.7%
11.85 1846
 
1.6%
18.23 1575
 
1.4%
7.39 1521
 
1.4%
16.11 1152
 
1.0%
15.23 1010
 
0.9%
8.72 921
 
0.8%
16.79 873
 
0.8%
Other values (6989) 95908
85.1%
ValueCountFrequency (%)
0 383
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 4
 
< 0.1%
0.06 11
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

Interactions

2023-06-23T10:25:45.718005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:29.969397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.551944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.115982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.630915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:37.539262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.263529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.884766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.516223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.197225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:45.860130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.137907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.707555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.276926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.796563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:37.701260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.436942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.018684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.663876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.348788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.012980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.307846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.881961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.443824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:35.040141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:37.891549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.615099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.174045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.826608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.512721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.163543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.448801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.036185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.600343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:35.492018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.058127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.788482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.323316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.988117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.663352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.317821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.624896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.194120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.772866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:35.734586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.235740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.947117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.459771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:43.154800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.821929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.452209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.770114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.337271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:33.914220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:35.961658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.432394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.091053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.606694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:43.304008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.961521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.592004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:30.945079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.488178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.056797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:36.308747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.603574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.243046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:41.827558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:43.462698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:45.108160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.732185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.083377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.625061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.207809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:36.871799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.766083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.417168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.036626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:43.737571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:45.257764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:46.876184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.250283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.801575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.360427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:37.125538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:38.931953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.578281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.229024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:43.900219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:45.418299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:47.015632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:31.408024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:32.953485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:34.504807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:37.369440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:39.098992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:40.749739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:42.379588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:44.053808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T10:25:45.575972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-06-23T10:25:52.225018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
product_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_item_idpricefreight_valueproduct_category_name
product_name_lenght1.0000.0740.1640.0760.062-0.0550.067-0.0200.0400.0340.131
product_description_lenght0.0741.0000.1100.095-0.0180.132-0.079-0.0320.2090.1170.203
product_photos_qty0.1640.1101.0000.0060.007-0.080-0.012-0.0660.0280.0100.152
product_weight_g0.0760.0950.0061.0000.6180.5330.620-0.0000.5140.4470.199
product_length_cm0.062-0.0180.0070.6181.0000.2500.6310.0060.2670.2840.259
product_height_cm-0.0550.132-0.0800.5330.2501.0000.3420.0180.3270.2820.278
product_width_cm0.067-0.079-0.0120.6200.6310.3421.000-0.0050.2710.2740.295
order_item_id-0.020-0.032-0.066-0.0000.0060.018-0.0051.000-0.117-0.0550.029
price0.0400.2090.0280.5140.2670.3270.271-0.1171.0000.4340.114
freight_value0.0340.1170.0100.4470.2840.2820.274-0.0550.4341.0000.093
product_category_name0.1310.2030.1520.1990.2590.2780.2950.0290.1140.0931.000

Missing values

2023-06-23T10:25:47.212029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-23T10:25:47.539830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-23T10:25:48.162231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_idorder_item_idseller_idshipping_limit_datepricefreight_value
01e9e8ef04dbcff4541ed26657ea517e5perfumaria40.0287.01.0225.016.010.014.0e17e4f88e31525f7deef66779844ddce15670f4db5b62c43d542e1b2d56b0cf7c2018-04-30 17:33:5410.917.39
13aa071139cb16b67ca9e5dea641aaa2fartes44.0276.01.01000.030.018.020.05236307716393b7114b53ee991f369561b561927807645834b59ef0d16ba55a242018-02-06 19:11:15248.0017.99
296bd76ec8810374ed1b65e291975717fesporte_lazer46.0250.01.0154.018.09.015.001f66e58769f84129811d43eefd187fb17b07b3c7487f0ea825fc6df75abd658b2018-07-11 21:30:2079.807.82
3cef67bcfe19066a932b7673e239eb23dbebes27.0261.01.0371.026.04.026.0143d00a4f2dde4e0364ee1821577adb31c510bc1718f0f2961eaa42a23330681a2018-08-07 09:10:13112.309.54
49dc1a7de274444849c219cff195d0b71utilidades_domesticas37.0402.04.0625.020.017.013.086cafb8794cb99a9b1b77fc8e48fbbbb10be8ff43f22e456b4e0371b2245e4d012018-04-17 01:30:2337.908.29
541d3672d4792049fa1779bb35283ed13instrumentos_musicais60.0745.01.0200.038.05.011.0c214058828b43a44f352b56ff2d5c0a51ce248b21cb2adc36282ede306b7660e52018-03-19 15:48:2945.8719.95
6732bd381ad09e530fe0a5f457d81becbcool_stuff56.01272.04.018350.070.024.044.09632facd8bd95315d63a23bf616d85b018b8cfc8305aa441e4239358c9f6f24852018-01-18 12:39:24958.0027.76
7732bd381ad09e530fe0a5f457d81becbcool_stuff56.01272.04.018350.070.024.044.0c6343db6c1801f9c3301166f0293111618b8cfc8305aa441e4239358c9f6f24852017-12-11 11:30:51968.0044.30
82548af3e6e77a690cf3eb6368e9ab61emoveis_decoracao56.0184.02.0900.040.08.040.06ecf7023e8dd4ec8b08746c35b9fcb60395f83f51203c626648c875dd41874c7f2017-11-30 16:31:009.9916.02
92548af3e6e77a690cf3eb6368e9ab61emoveis_decoracao56.0184.02.0900.040.08.040.06ecf7023e8dd4ec8b08746c35b9fcb60495f83f51203c626648c875dd41874c7f2017-11-30 16:31:009.9916.02
product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_idorder_item_idseller_idshipping_limit_datepricefreight_value
1126409a7c6041fa9592d9d9ef6cfe62a71f8ccama_mesa_banho50.0799.01.01400.027.07.027.07c8a032bb75e0e4d524b14ba147d4ba51439a47cc365d6e3bd526812ea9de3c292017-08-25 15:50:13127.5017.14
1126419a7c6041fa9592d9d9ef6cfe62a71f8ccama_mesa_banho50.0799.01.01400.027.07.027.0fc957026f2482ab3bddf91ebc9d0dfc51e4ebd3f87bf70440014f07ddda7fbe032018-01-16 21:31:29127.0012.39
11264283808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.0420937423f0cb3d3fe689330b5d385a914324dd16853115efb0fd9d0d131ba6f42017-10-26 02:49:1339.7116.11
11264383808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.0420937423f0cb3d3fe689330b5d385a924324dd16853115efb0fd9d0d131ba6f42017-10-26 02:49:1339.7116.11
11264483808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.059e88b7d3675e89aceaf86f372d3bc9a14324dd16853115efb0fd9d0d131ba6f42018-01-10 03:32:0929.9016.11
11264583808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.059e88b7d3675e89aceaf86f372d3bc9a24324dd16853115efb0fd9d0d131ba6f42018-01-10 03:32:0929.9016.11
11264683808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.06e4465d771f02e4fe335225de3c6c04314324dd16853115efb0fd9d0d131ba6f42018-03-14 02:30:4829.9023.28
11264783808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.06e4465d771f02e4fe335225de3c6c04324324dd16853115efb0fd9d0d131ba6f42018-03-14 02:30:4829.9023.28
11264883808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.06e4465d771f02e4fe335225de3c6c04334324dd16853115efb0fd9d0d131ba6f42018-03-14 02:30:4829.9023.28
112649106392145fca363410d287a815be6de4cama_mesa_banho58.0309.01.02083.012.02.07.0f3a47ba087f05d39a74ed1b653f0be1b14a3ca9315b744ce9f8e93743614938842018-07-02 08:30:34107.5027.05